Table 3.

Genomics and transcriptomic tool with their uses in SM study of white rot fungi

Tool and techniques	Mechanism	Application	References
Genomic techniques
DNA microarray	Hybridization based genomic technology identified gene from gene clusters	Prediction of specific protein and metabolite sequences by comparing with another genome	Özdemir et al. (2017)
Genome mining	Locate the genes into the genome	Search the location of genes that helps in metabolite formation	Narayanan et al. (2010)
	Screening of unknown genes from whole-genome sequence	To know the biosynthetic potential of fungal SM BGC	Palazzotto and Weber (2018)
Global Natural Product Social Molecular Networking (GNPS)	Added with MS/MS spectrum coupled with GC/LC to know the natural products	Perform MS searches using MS/MS spectrum as a query search, help in the quantification of metabolites or other drug components into the sample	Mao et al. (2021)
CRISPR/Cas9	Based on genome editing technology	Highly efficient genetic manipulation technique with enabled the taking advantage and discovery of new bioactive compounds	Hadjithomas et al. (2017)
Transcriptomic approach
cDNA-AFLP	Sequence needed for cluster and alignment	Discover novel genes for metabolite production	Garber et al. (2011)
Shotgun sequencing	RNA identification by forming cDNA fragments	Detect, quantify, and annotate the coding/non-coding RNA	Fondi and Liò (2015)
Probe-based arrays	mRNA analysis with labeled sample and connect with cap analysis of gene expression tool (CAGE)	Explore gene expression at global level, screening of SM BGC cluster using Southern blots	Hasin et al. (2017)
Deep-sequencing technologies	RNA-sequencing for SM gene prediction in fungi	Determine RNA expression level, capture transcriptome dynamics	Ozsolak and Milos (2011)
Next-generation sequence (NGS)	RNA sequence identification by alter the DNA sequencing	Biomarker, therapeutic targets, SM gene cluster verification	Liu et al. (2021); Hasin et al. (2017)